In this lesson, we will learn how to import a larger dataset, and test our skills cleaning and plotting the data.
After completing this tutorial, you will be able to:
- Subset data using the dplyr
dplyrpipes to manipulate data in
- Describe what a pipe does and how it is used to manipulate data in
What you need
RStudio to complete this tutorial. Also we recommend that you have an
earth-analytics directory setup on your computer with a
/data directory within it.
R Libraries to Install:
Important - Data Organization
Before you begin this lesson, be sure that you’ve downloaded the dataset above. You will need to UNZIP the zip file. When you do this, be sure that your directory looks like the image below: note that all of the data are within the week2 directory. They are not nested within another directory. You may have to copy and paste your files to make this look right.
Get started with time series data
To begin, load the
dplyr libraries. Also, set your working directory. Finally, set
FALSE globally using
options(stringsAsFactors = FALSE).
# set your working directory to the earth-analytics directory # setwd("working-dir-path-here") # load packages library(ggplot2) library(lubridate) ## ## Attaching package: 'lubridate' ## The following object is masked from 'package:base': ## ## date library(dplyr) # set strings as factors to false options(stringsAsFactors = FALSE)
Import precipitation time series
We will use a precipitation dataset collected by the National Centers for Environmental Information (formerly National Climate Data Center) Cooperative Observer Network (COOP) station 050843 in Boulder, CO. The data cover the time span between 1 January 2003 through 31 December 2013.
To begin, use
read.csv() to import the
# download the data # download.file(url = "https://ndownloader.figshare.com/files/7283285", # destfile = "data/week_02/805325-precip-dailysum_2003-2013.csv") # import the data boulder_daily_precip <- read.csv("data/week_02/precipitation/805325-precip-dailysum-2003-2013.csv", header = TRUE) # view first 6 lines of the data head(boulder_daily_precip) ## DATE DAILY_PRECIP STATION STATION_NAME ELEVATION LATITUDE ## 1 1/1/03 0.00 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## 2 1/5/03 999.99 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## 3 2/1/03 0.00 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## 4 2/2/03 999.99 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## 5 2/3/03 0.40 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## 6 2/5/03 0.20 COOP:050843 BOULDER 2 CO US 1650.5 40.03389 ## LONGITUDE YEAR JULIAN ## 1 -105.2811 2003 1 ## 2 -105.2811 2003 5 ## 3 -105.2811 2003 32 ## 4 -105.2811 2003 33 ## 5 -105.2811 2003 34 ## 6 -105.2811 2003 36 # view structure of data str(boulder_daily_precip) ## 'data.frame': 792 obs. of 9 variables: ## $ DATE : chr "1/1/03" "1/5/03" "2/1/03" "2/2/03" ... ## $ DAILY_PRECIP: num 0e+00 1e+03 0e+00 1e+03 4e-01 ... ## $ STATION : chr "COOP:050843" "COOP:050843" "COOP:050843" "COOP:050843" ... ## $ STATION_NAME: chr "BOULDER 2 CO US" "BOULDER 2 CO US" "BOULDER 2 CO US" "BOULDER 2 CO US" ... ## $ ELEVATION : num 1650 1650 1650 1650 1650 ... ## $ LATITUDE : num 40 40 40 40 40 ... ## $ LONGITUDE : num -105 -105 -105 -105 -105 ... ## $ YEAR : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ... ## $ JULIAN : int 1 5 32 33 34 36 37 38 41 49 ... # are there any unusual / No data values? summary(boulder_daily_precip$DAILY_PRECIP) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.000 0.100 0.100 5.297 0.300 999.990 max(boulder_daily_precip$DAILY_PRECIP) ##  999.99
About the Data
Viewing the structure of these data, we can see that different types of data are included in this file:
- STATION and STATION_NAME: Identification of the COOP station.
- ELEVATION, LATITUDE and LONGITUDE: The spatial location of the station.
- DATE: The date when the data were collected in the format: YYYYMMDD. Notice that DATE is currently class
chr, meaning the data is interpreted as a character class and not as a date.
- DAILY_PRECIP: The total precipitation in inches. Important: the metadata notes that the value 999.99 indicates missing data. Also important, hours with no precipitation are not recorded.
- YEAR: The year the data were collected.
- JULIAN: The JULIAN DAY the data were collected.
Additional information about the data, known as metadata, is available in the PRECIP_HLY_documentation.pdf. The metadata tell us that the noData value for these data is 999.99. IMPORTANT: We have modified these data a bit for ease of teaching and learning. Specifically, we’ve aggregated the data to represent daily sum values and added some noData values to ensure you learn how to clean them!
You can download the original complete data subset with additional documentation here.
Using everything you’ve learned in the previous lessons:
- Import the dataset:
- Clean the data by assigning noData values to
- Make sure the date column is a date class
- When you are done, plot it using
- Be sure to include a TITLE, and label the X and Y axes
- Change the color of the plotted points
Some notes to help you along:
- Date: Be sure to take off the date format when you import the data.
- NoData Values: We know that the no data value = 999.99. We can account for this when we read in the data. Remember how?
Your final plot should look something like the plot below.
Import data and reassign na values
To begin, import the data. Be sure to use the
na.strings argument to remove
NA values. Also our data have a header (the first row represents column names) so set
header = TRUE
# import data boulder_daily_precip <- read.csv("data/week_02/precipitation/805325-precip-dailysum-2003-2013.csv", header = TRUE, na.strings = 999.99)
Next, take care of the date field. In this case we have month/day/year. We can use
?strptime to figure out which letters we need to use in the format = argument to ensure our data elements (month, day and year) are understood by
In this case we want to use
- %m - for month
- %d - for day
- %y - for year
Also take note of the format of our date. In this case, each date element is separated by a
# format date field boulder_daily_precip$DATE <- as.Date(boulder_daily_precip$DATE, format = "%m/%d/%y")
Finally, we can plot the data using
ggplot(). Notice that when we plot, we first populate the
- data = contain the data frame that we want to plot
- aes = contain the x and y variables that we want to plot.
geom_point() represents the geometry that you want to plot. In this case you are creating a scatter plot (using points).
# plot the data using ggplot2 ggplot(data=boulder_daily_precip, aes(x = DATE, y = DAILY_PRECIP)) + geom_point() + labs(title = "Precipitation - Boulder, Colorado")
NA values and warnings
When we plot the data, we get a warning that says:
## Warning: Removed 4 rows containing missing values (geom_point).
We can get rid of this warning by removing NA or missing data values from our data. A warning is just
R’s way of letting you know that something may be wrong. In this case, it can’t plot 4 data points because there are missing data values there.
Let’s remove the missing data value rows using a
dplyr pipe and the
na.omit() function. We will talk about pipes in just a minute!
boulder_daily_precip <- boulder_daily_precip %>% na.omit()
Now we can plot the data without a warning!
# plot the data using ggplot2 ggplot(data=boulder_daily_precip, aes(x = DATE, y = DAILY_PRECIP)) + geom_point(color = "darkorchid4") + labs(title = "Precipitation - Boulder, Colorado")
Don’t forget to add x and y axis labels to your plot! Use the
labs() function to add a title, x and y label (and subtitle if you’d like) to your plot.
labs(title = "Hourly Precipitation - Boulder Station\n 2003-2013", x = "Date", y = "Precipitation (Inches)")
ggplot(data = boulder_daily_precip, aes(DATE, DAILY_PRECIP)) + geom_point(color = "darkorchid4") + labs(title = "Hourly Precipitation - Boulder Station\n 2003-2013", x = "Date", y = "Precipitation (Inches)")
You can add a ggplot theme to adjust the look of your plot quickly too. Below we use
theme_bw(). Below we also adjust the base font size to make the labels a bit larger
base_size = 11.
Data Tip: Learn more about built in ggplot themes
ggplot(data = boulder_daily_precip, aes(DATE, DAILY_PRECIP)) + geom_point(color = "darkorchid4") + labs(title = "Hourly Precipitation - Boulder Station\n 2003-2013", x = "Date", y = "Precipitation (Inches)") + theme_bw(base_size = 11)
Data Tip: For a more thorough review of date/time classes, see the NEON tutorial Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt.
Take a close look at the plot:
- What does each point represent?
- Use the
max()functions to determine the minimum and maximum precipitation values for the 10 year span?
Introduction to the pipe %>%
Above we used pipes to manipulate our data. Specifically we removed
NA values in a pipe with
Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same data set. Pipes in
R look like
%>% and are made available via the
magrittr package, installed automatically with
boulder_daily_precip <- boulder_daily_precip %>% na.omit()
Pipes are nice to use when coding because:
- they remove intermediately created variables (keeping our environment cleaner / fewer variables are saved memory)
- they combine multiple steps of processing into a clean set of steps that is easy to read once you become familiar with the pipes syntax
We can do all of the same things that we did above with one pipe. Let’s see how:
# format date field without pipes boulder_daily_precip$DATE <- as.Date(boulder_daily_precip$DATE, format = "%m/%d/%y")
With pipes we can use the mutate function to either create a new column or modify the format or contents of an existing column.
boulder_daily_precip <- boulder_daily_precip %>% mutate(DATE = as.Date(DATE, format = "%m/%d/%y"))
We can then add the
na.omit() function to the above code
boulder_daily_precip <- boulder_daily_precip %>% mutate(DATE = as.Date(DATE, format = "%m/%d/%y")) %>% na.omit()
Notice that each time you assign the pipe to a variable, you are overwriting that variable.
boulder_daily_precip <- boulder_daily_precip
In this case we are just updating our current
The process above avoids processing the data in separate steps, and potentially creating new variables each time. We can even send the output to ggplot(). When we send output to ggplot() in a pipe, we don’t need the use the data argument (
data = boulder_daily_precip) because we send the data throught the pipe. Like this:
Note: that because you are creating a plot with the code below, you don’t need to assign the pipe to a variable. Thus you leave out the
boulder_daily_precip %>% mutate(DATE = as.Date(boulder_daily_precip$DATE, format = "%m/%d/%y")) %>% na.omit() %>% ggplot(aes(DATE, DAILY_PRECIP)) + geom_point(color = "darkorchid4") + labs(title = "Hourly Precipitation - Boulder Station\n 2003-2013", subtitle = "plotted using pipes", x = "Date", y = "Precipitation (Inches)") + theme_bw()
Subset the data
You may want to only work with a subset of your time series data. Let’s create a subset of data for the time period around the flood between 15 August to 15 October 2013. We use the
filter() function in the
dplyr package to do this and pipes!
# subset 2 months around flood precip_boulder_AugOct <- boulder_daily_precip %>% filter(DATE >= as.Date('2013-08-15') & DATE <= as.Date('2013-10-15'))
In the code above, we use the pipe to send the
boulder_daily_precip data through a filter step. In that filter step, we filter out only the rows within the date range that we specified. Since
%>% takes the object on its left and passes it as the first argument to the function on its right, we don’t need to explicitly include it as an argument to the
# check the first & last dates min(precip_boulder_AugOct$DATE) ##  "2013-08-21" max(precip_boulder_AugOct$DATE) ##  "2013-10-11" # create new plot ggplot(data = precip_boulder_AugOct, aes(DATE,DAILY_PRECIP)) + geom_bar(stat = "identity", fill = "darkorchid4") + xlab("Date") + ylab("Precipitation (inches)") + ggtitle("Daily Total Precipitation Aug - Oct 2013 for Boulder Creek") + theme_bw()
Create a subset from the same dates in 2012 to compare to the 2013 plot. Use the
ylim() argument to ensure the y axis range is the SAME as the previous plot - from 0 to 10”.
How different was the rainfall in 2012?
?lims in the console to see how the
ylim arguments work.